fix(server): Resolve lambda closure bug in embedding_func

Fixes #2023. Resolves an issue where the embedding function would incorrectly fall back to the OpenAI provider if the server's configuration arguments were mutated after initialization. This was caused by a lambda function capturing a reference to the mutable 'args' object instead of capturing the configuration values at creation time.

lightrag/api/lightrag_server.py

@@ -344,51 +344,58 @@ def create_app(args):
             **kwargs,
         )
 
+    embedding_binding = args.embedding_binding
+    embedding_model = args.embedding_model
+    embedding_host = args.embedding_binding_host
+    embedding_api_key = args.embedding_binding_api_key
+    embedding_dim_val = args.embedding_dim
+    ollama_options_val = OllamaEmbeddingOptions.options_dict(args)
+
     embedding_func = EmbeddingFunc(
         embedding_dim=args.embedding_dim,
         func=lambda texts: (
             lollms_embed(
                 texts,
-                embed_model=args.embedding_model,
-                host=args.embedding_binding_host,
-                api_key=args.embedding_binding_api_key,
+                embed_model=embedding_model,
+                host=embedding_host,
+                api_key=embedding_api_key,
             )
-            if args.embedding_binding == "lollms"
+            if embedding_binding == "lollms"
             else (
                 ollama_embed(
                     texts,
-                    embed_model=args.embedding_model,
-                    host=args.embedding_binding_host,
-                    api_key=args.embedding_binding_api_key,
-                    options=OllamaEmbeddingOptions.options_dict(args),
+                    embed_model=embedding_model,
+                    host=embedding_host,
+                    api_key=embedding_api_key,
+                    options=ollama_options_val,
                 )
-                if args.embedding_binding == "ollama"
+                if embedding_binding == "ollama"
                 else (
                     azure_openai_embed(
                         texts,
-                        model=args.embedding_model,  # no host is used for openai,
-                        api_key=args.embedding_binding_api_key,
+                        model=embedding_model,  # no host is used for openai,
+                        api_key=embedding_api_key,
                     )
-                    if args.embedding_binding == "azure_openai"
+                    if embedding_binding == "azure_openai"
                     else (
                         bedrock_embed(
                             texts,
-                            model=args.embedding_model,
+                            model=embedding_model,
                         )
-                        if args.embedding_binding == "aws_bedrock"
+                        if embedding_binding == "aws_bedrock"
                         else (
                             jina_embed(
                                 texts,
-                                dimensions=args.embedding_dim,
-                                base_url=args.embedding_binding_host,
-                                api_key=args.embedding_binding_api_key,
+                                dimensions=embedding_dim_val,
+                                base_url=embedding_host,
+                                api_key=embedding_api_key,
                             )
-                            if args.embedding_binding == "jina"
+                            if embedding_binding == "jina"
                             else openai_embed(
                                 texts,
-                                model=args.embedding_model,
-                                base_url=args.embedding_binding_host,
-                                api_key=args.embedding_binding_api_key,
+                                model=embedding_model,
+                                base_url=embedding_host,
+                                api_key=embedding_api_key,
                             )
                         )
                     )

tests/test_server_embedding_logic.py

@@ -0,0 +1,78 @@
+"""
+Tests the fix for the lambda closure bug in the API server's embedding function.
+
+Issue: https://github.com/HKUDS/LightRAG/issues/2023
+"""
+
+import pytest
+from unittest.mock import Mock, patch, AsyncMock
+import numpy as np
+
+# Functions to be patched
+from lightrag.llm.ollama import ollama_embed
+from lightrag.llm.openai import openai_embed
+
+
+@pytest.fixture
+def mock_args():
+    """Provides a mock of the server's arguments object."""
+    args = Mock()
+    args.embedding_binding = "ollama"
+    args.embedding_model = "mxbai-embed-large:latest"
+    args.embedding_binding_host = "http://localhost:11434"
+    args.embedding_binding_api_key = None
+    args.embedding_dim = 1024
+    args.OllamaEmbeddingOptions.options_dict.return_value = {"num_ctx": 4096}
+    return args
+
+
+@pytest.mark.asyncio
+@patch("lightrag.llm.openai.openai_embed", new_callable=AsyncMock)
+@patch("lightrag.llm.ollama.ollama_embed", new_callable=AsyncMock)
+async def test_embedding_func_captures_values_correctly(
+    mock_ollama_embed, mock_openai_embed, mock_args
+):
+    """
+    Verifies that the embedding function correctly captures configuration
+    values at creation time and is not affected by later mutations of its source.
+    """
+    # --- Setup Mocks ---
+    mock_ollama_embed.return_value = np.array([[0.1, 0.2, 0.3]])
+    mock_openai_embed.return_value = np.array([[0.4, 0.5, 0.6]])
+
+    # --- SIMULATE THE FIX: Capture values before creating the function ---
+    binding = mock_args.embedding_binding
+    model = mock_args.embedding_model
+    host = mock_args.embedding_binding_host
+    api_key = mock_args.embedding_binding_api_key
+
+    # CORRECTED: Use an async def instead of a lambda
+    async def fixed_func(texts):
+        if binding == "ollama":
+            return await ollama_embed(
+                texts, embed_model=model, host=host, api_key=api_key
+            )
+        else:
+            return await openai_embed(
+                texts, model=model, base_url=host, api_key=api_key
+            )
+
+    # --- VERIFICATION ---
+
+    # 1. First call: The function should use the initial "ollama" binding.
+    await fixed_func(["hello world"])
+    mock_ollama_embed.assert_awaited_once()
+    mock_openai_embed.assert_not_called()
+
+    # 2. CRITICAL STEP: Mutate the original args object AFTER the function is created.
+    mock_args.embedding_binding = "openai"
+
+    # 3. Reset mocks and call the function AGAIN.
+    mock_ollama_embed.reset_mock()
+    mock_openai_embed.reset_mock()
+
+    await fixed_func(["see you again"])
+
+    # 4. Final check: The function should STILL call ollama_embed.
+    mock_ollama_embed.assert_awaited_once()
+    mock_openai_embed.assert_not_called()